FIA NLS Models: Biomass Growth vs. Biomass – Data Subsets
JAH
2023-04-20
Background
This analysis document compliments FIA NLS Models: Biomass Growth vs. Biomass. All of the background information from that document applies to these analyses, which are extensions to them. The difference between that document and this analysis is the use of different data subsets.
Here, we fit the models using: 1) a temporally-balanced dataset, where we take the first and most-recent plot record for all plots in the dataset, 2) a temporally-balanced dataset (same as #1), but which excludes plot locations which have experienced harvest (at any point over the study interval 2000-2022)
Below the model fitting procedure is implemented by ecoprovince:
Temporally-balancing the biomass growth data set
Lets look at some quick attributes of the dataset
- The data set has 115221 observations, comprised of 58079 plots.
- The frequency of growth measurements among plots is as follows (n=1 through 5): 25558, 13784, 12967, 5656, 114.
- Thus 55.99% of plots have at least two growth measurements.
Analysis 1: Temporally-balanced analysis
211 - Northeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4795 4394.3
## 2 4794 4208.3 1 185.97 211.86 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 18612.85
## 2 2 18407.37##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.12796 0.16873 0.758 0.448
## alpha 0.63217 0.04075 15.514 <2e-16 ***
## A 3.59033 0.12724 28.216 <2e-16 ***
## k 7.38833 0.78431 9.420 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9369 on 4794 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.092e-06
## (36 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_211, :
## object 'ge.fit' not found
## model AIC
## 1 2 18407.37
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.12796 0.16873 0.758 0.448
## alpha 0.63217 0.04075 15.514 <2e-16 ***
## A 3.59033 0.12724 28.216 <2e-16 ***
## k 7.38833 0.78431 9.420 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9369 on 4794 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.092e-06
## (36 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92687, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -8.4996, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: Removed 21 rows containing missing values (`geom_point()`).## Warning: Removed 1050 rows containing missing values (`geom_line()`).
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 9777 9697.8
## 2 9776 9072.0 1 625.8 674.36 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 35844.15
## 2 2 35193.76##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.26421 0.21330 5.927 3.19e-09 ***
## alpha 0.81079 0.02855 28.402 < 2e-16 ***
## A 2.53536 0.09182 27.611 < 2e-16 ***
## k 10.23482 0.59728 17.136 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9633 on 9776 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.167e-06
## (3196 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_212, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_212, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_212, :
## object 'ge.fit' not found
## model AIC
## 1 2 35193.76
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.26421 0.21330 5.927 3.19e-09 ***
## alpha 0.81079 0.02855 28.402 < 2e-16 ***
## A 2.53536 0.09182 27.611 < 2e-16 ***
## k 10.23482 0.59728 17.136 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9633 on 9776 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.167e-06
## (3196 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: Removed 1578 rows containing missing values (`geom_point()`).## Warning: Removed 1031 rows containing missing values (`geom_line()`).
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5424 7458.3
## 2 5423 7181.4 1 276.9 209.1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 24022.47
## 2 2 23819.15##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.74246 0.13710 -5.416 6.37e-08 ***
## alpha 0.71420 0.04645 15.376 < 2e-16 ***
## A 5.15045 0.18927 27.212 < 2e-16 ***
## k 15.74832 1.96440 8.017 1.32e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.151 on 5423 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 6.78e-06
## (35 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_221, :
## object 'ge.fit' not found
## model AIC
## 1 2 23819.15
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.74246 0.13710 -5.416 6.37e-08 ***
## alpha 0.71420 0.04645 15.376 < 2e-16 ***
## A 5.15045 0.18927 27.212 < 2e-16 ***
## k 15.74832 1.96440 8.017 1.32e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.151 on 5423 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 6.78e-06
## (35 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 16 rows containing missing values (`geom_point()`).## Warning: Removed 1036 rows containing missing values (`geom_line()`).
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2742 3071.9
## 2 2741 2862.1 1 209.82 200.94 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 11096.73
## 2 2 10904.53##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.13761 0.27169 0.506 0.613
## alpha 0.84729 0.05419 15.635 <2e-16 ***
## A 4.27341 0.25025 17.077 <2e-16 ***
## k 20.20001 2.07777 9.722 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.022 on 2741 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 9.502e-06
## (809 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_222, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_222, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_222, :
## object 'ge.fit' not found
## model AIC
## 1 2 10904.53
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.13761 0.27169 0.506 0.613
## alpha 0.84729 0.05419 15.635 <2e-16 ***
## A 4.27341 0.25025 17.077 <2e-16 ***
## k 20.20001 2.07777 9.722 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.022 on 2741 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 9.502e-06
## (809 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90201, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.6265, p-value = 1.839e-08
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 422 rows containing missing values (`geom_point()`).## Warning: Removed 1108 rows containing missing values (`geom_line()`).
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5267 6923.8
## 2 5266 6738.2 1 185.68 145.11 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 22279.18
## 2 2 22137.93##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.05530 0.11325 -9.318 <2e-16 ***
## alpha 0.65905 0.05127 12.855 <2e-16 ***
## A 5.75637 0.22973 25.057 <2e-16 ***
## k 35.12926 3.60650 9.741 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.131 on 5266 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 9.487e-06
## (1120 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_223, :
## object 'ge.fit' not found
## model AIC
## 1 2 22137.93
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.05530 0.11325 -9.318 <2e-16 ***
## alpha 0.65905 0.05127 12.855 <2e-16 ***
## A 5.75637 0.22973 25.057 <2e-16 ***
## k 35.12926 3.60650 9.741 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.131 on 5266 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 9.487e-06
## (1120 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 558 rows containing missing values (`geom_point()`).## Warning: Removed 1002 rows containing missing values (`geom_line()`).
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8057 19562
## 2 8056 17896 1 1665.9 749.93 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 42193.22
## 2 2 41477.82##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.84976 0.17604 4.827 1.41e-06 ***
## alpha 0.87179 0.02897 30.091 < 2e-16 ***
## A 4.53035 0.14034 32.281 < 2e-16 ***
## k 1.77001 0.27791 6.369 2.01e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.49 on 8056 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 8.422e-06
## (140 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_231, :
## object 'ge.fit' not found
## model AIC
## 1 2 41477.82
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.84976 0.17604 4.827 1.41e-06 ***
## alpha 0.87179 0.02897 30.091 < 2e-16 ***
## A 4.53035 0.14034 32.281 < 2e-16 ***
## k 1.77001 0.27791 6.369 2.01e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.49 on 8056 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 8.422e-06
## (140 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 72 rows containing missing values (`geom_point()`).## Warning: Removed 1017 rows containing missing values (`geom_line()`).
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8011 21190
## 2 8010 19445 1 1745.4 719.01 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 41553.79
## 2 2 40866.90##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.69287 0.18809 3.684 0.000231 ***
## alpha 0.87132 0.02918 29.863 < 2e-16 ***
## A 4.59318 0.16365 28.068 < 2e-16 ***
## k 7.19093 0.65146 11.038 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.558 on 8010 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 5.236e-06
## (180 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_232, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_232, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_232, :
## object 'ge.fit' not found
## model AIC
## 1 2 40866.9
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.69287 0.18809 3.684 0.000231 ***
## alpha 0.87132 0.02918 29.863 < 2e-16 ***
## A 4.59318 0.16365 28.068 < 2e-16 ***
## k 7.19093 0.65146 11.038 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.558 on 8010 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 5.236e-06
## (180 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 87 rows containing missing values (`geom_point()`).## Warning: Removed 931 rows containing missing values (`geom_line()`).
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 830 2260.6
## 2 829 2131.2 1 129.37 50.321 2.801e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4393.881
## 2 2 4346.793##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.8587 0.8208 1.046 0.296
## alpha 0.8321 0.1053 7.902 8.70e-15 ***
## A 4.0748 0.6206 6.566 9.11e-11 ***
## k 1.5596 1.1821 1.319 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.603 on 829 degrees of freedom
##
## Number of iterations to convergence: 23
## Achieved convergence tolerance: 8.221e-06
## (29 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_234, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_234, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_234, :
## object 'ge.fit' not found
## model AIC
## 1 2 4346.793
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.8587 0.8208 1.046 0.296
## alpha 0.8321 0.1053 7.902 8.70e-15 ***
## A 4.0748 0.6206 6.566 9.11e-11 ***
## k 1.5596 1.1821 1.319 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.603 on 829 degrees of freedom
##
## Number of iterations to convergence: 23
## Achieved convergence tolerance: 8.221e-06
## (29 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.91018, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.0259, p-value = 0.002479
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 15 rows containing missing values (`geom_point()`).## Warning: Removed 645 rows containing missing values (`geom_line()`).
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_242$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_242.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 979 1429.8
## 2 978 1418.5 1 11.301 7.7913 0.005352 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4162.017
## 2 2 4156.224##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.02833 0.53460 -0.053 0.95775
## alpha 0.45520 0.15511 2.935 0.00342 **
## A 3.43217 0.41750 8.221 6.39e-16 ***
## k 12.15390 3.79888 3.199 0.00142 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.204 on 978 degrees of freedom
##
## Number of iterations to convergence: 13
## Achieved convergence tolerance: 6.251e-06
## (412 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_251, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_251, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_251, :
## object 'ge.fit' not found
## model AIC
## 1 2 4156.224
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.02833 0.53460 -0.053 0.95775
## alpha 0.45520 0.15511 2.935 0.00342 **
## A 3.43217 0.41750 8.221 6.39e-16 ***
## k 12.15390 3.79888 3.199 0.00142 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.204 on 978 degrees of freedom
##
## Number of iterations to convergence: 13
## Achieved convergence tolerance: 6.251e-06
## (412 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.71959, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.9504, p-value = 0.05113
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 220 rows containing missing values (`geom_point()`).## Warning: Removed 1176 rows containing missing values (`geom_line()`).
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Error in nls(fg_1, data = G_255, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_255, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_255$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_255.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
add p model: does not fit
add s model: does not fit
add s+p model: does not fit
note: model fit, but fit was funky due to data being sparse
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_313.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
model selection 1
## Error in nls(fg_1, data = G_331, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_331, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_331.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
332 - Great Plains Steppe
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 136 120.26
## 2 135 117.52 1 2.7361 3.143 0.07851 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 459.1608
## 2 2 457.9617##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.5868 2.5256 0.628 0.5309
## alpha 0.5938 0.3038 1.955 0.0527 .
## A 2.9848 1.3104 2.278 0.0243 *
## k 58.3210 24.2463 2.405 0.0175 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.933 on 135 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 1.529e-06
## (15 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_332, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_332, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_332, :
## object 'ge.fit' not found
## model AIC
## 1 2 457.9617
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.5868 2.5256 0.628 0.5309
## alpha 0.5938 0.3038 1.955 0.0527 .
## A 2.9848 1.3104 2.278 0.0243 *
## k 58.3210 24.2463 2.405 0.0175 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.933 on 135 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 1.529e-06
## (15 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90099, p-value = 3.897e-08
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.3818, p-value = 0.167
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 7 rows containing missing values (`geom_point()`).## Warning: Removed 1140 rows containing missing values (`geom_line()`).
plotting 2
341 - Intermountain Semi-desert & Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
model selection 1
## Error in nls(fg_1, data = G_342, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_342, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_342$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_342.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
411 - Everglades
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5087 4233.1
## 2 5086 3997.3 1 235.83 300.06 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 19222.23
## 2 2 18932.46##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.8685 0.2251 3.858 0.000116 ***
## alpha 0.6406 0.0345 18.570 < 2e-16 ***
## A 2.9351 0.1209 24.285 < 2e-16 ***
## k 2.8562 0.4807 5.942 3e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8865 on 5086 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 4.237e-06
## (14 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## model AIC
## 1 2 18932.46
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.8685 0.2251 3.858 0.000116 ***
## alpha 0.6406 0.0345 18.570 < 2e-16 ***
## A 2.9351 0.1209 24.285 < 2e-16 ***
## k 2.8562 0.4807 5.942 3e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8865 on 5086 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 4.237e-06
## (14 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 8 rows containing missing values (`geom_point()`).## Warning: Removed 1108 rows containing missing values (`geom_line()`).
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5232 11548
## 2 5231 11295 1 252.68 117.02 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 25880.58
## 2 2 25766.76##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.09923 0.19061 -0.521 0.603
## alpha 0.82689 0.07218 11.456 < 2e-16 ***
## A 4.38260 0.19106 22.938 < 2e-16 ***
## k 9.42197 2.10536 4.475 7.79e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.469 on 5231 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.702e-06
## (27 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## model AIC
## 1 2 25766.76
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.09923 0.19061 -0.521 0.603
## alpha 0.82689 0.07218 11.456 < 2e-16 ***
## A 4.38260 0.19106 22.938 < 2e-16 ***
## k 9.42197 2.10536 4.475 7.79e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.469 on 5231 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.702e-06
## (27 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 20 rows containing missing values (`geom_point()`).## Warning: Removed 982 rows containing missing values (`geom_line()`).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 598 960.81
## 2 597 931.59 1 29.223 18.727 1.768e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2572.128
## 2 2 2555.565##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 3.0863 1.6829 1.834 0.0672 .
## alpha 0.9648 0.2063 4.678 3.59e-06 ***
## A 1.8964 0.4344 4.366 1.49e-05 ***
## k 9.2756 6.2080 1.494 0.1357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.249 on 597 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 5.796e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## model AIC
## 1 2 2555.565
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 3.0863 1.6829 1.834 0.0672 .
## alpha 0.9648 0.2063 4.678 3.59e-06 ***
## A 1.8964 0.4344 4.366 1.49e-05 ***
## k 9.2756 6.2080 1.494 0.1357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.249 on 597 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 5.796e-06
## (3 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93069, p-value = 4.695e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.5524, p-value = 0.1206
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1175 rows containing missing values (`geom_line()`).
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 668 936.72
## 2 667 894.41 1 42.31 31.552 2.852e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2792.478
## 2 2 2763.464##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 6.6083 3.7929 1.742 0.08192 .
## alpha 0.9277 0.1535 6.042 2.52e-09 ***
## A 1.2364 0.4243 2.914 0.00369 **
## k 3.4720 2.0231 1.716 0.08660 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.158 on 667 degrees of freedom
##
## Number of iterations to convergence: 14
## Achieved convergence tolerance: 8.399e-06
## (7 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## model AIC
## 1 2 2763.464
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 6.6083 3.7929 1.742 0.08192 .
## alpha 0.9277 0.1535 6.042 2.52e-09 ***
## A 1.2364 0.4243 2.914 0.00369 **
## k 3.4720 2.0231 1.716 0.08660 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.158 on 667 degrees of freedom
##
## Number of iterations to convergence: 14
## Achieved convergence tolerance: 8.399e-06
## (7 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.95943, p-value = 1.172e-12
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.5452, p-value = 0.0003923
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 6 rows containing missing values (`geom_point()`).## Warning: Removed 1218 rows containing missing values (`geom_line()`).
plotting 2
M242 - Cascade Mixed Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 165 194.45
## 2 164 191.09 1 3.3559 2.8801 0.09158 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 584.8960
## 2 2 583.9712##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.4848 2.5848 -0.574 0.566
## alpha 0.6877 0.3770 1.824 0.070 .
## A 14.5055 15.3585 0.944 0.346
## k 350.4542 304.9435 1.149 0.252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.079 on 164 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 1.983e-06
## (172 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## model AIC
## 1 2 583.9712
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.4848 2.5848 -0.574 0.566
## alpha 0.6877 0.3770 1.824 0.070 .
## A 14.5055 15.3585 0.944 0.346
## k 350.4542 304.9435 1.149 0.252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.079 on 164 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 1.983e-06
## (172 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96329, p-value = 0.0002053
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 1.4867, p-value = 0.1371
## alternative hypothesis: two.sidedpredict and plot
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: Removed 86 rows containing missing values (`geom_point()`).## Warning: Removed 1274 rows containing missing values (`geom_line()`).
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_M313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M313.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_M331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M331.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 217 220.35
## 2 216 199.78 1 20.567 22.236 4.324e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 664.8357
## 2 2 645.2795##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.2157 1.5963 0.135 0.89262
## alpha 0.8899 0.1621 5.490 1.12e-07 ***
## A 2.5535 0.9059 2.819 0.00527 **
## k 39.5775 14.6677 2.698 0.00752 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9617 on 216 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 5.903e-06
## (90 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## model AIC
## 1 2 645.2795
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.2157 1.5963 0.135 0.89262
## alpha 0.8899 0.1621 5.490 1.12e-07 ***
## A 2.5535 0.9059 2.819 0.00527 **
## k 39.5775 14.6677 2.698 0.00752 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9617 on 216 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 5.903e-06
## (90 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.72946, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.22674, p-value = 0.8206
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 45 rows containing missing values (`geom_point()`).## Warning: Removed 1264 rows containing missing values (`geom_line()`).
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 2 |
| 212 | Laurentian Mixed Forest | 2 |
| 221 | Eastern Broadleaf Forest | 2 |
| 222 | Midwest Broadleaf Forest | 2 |
| 223 | Central Interior Broadleaf Forest | 2 |
| 231 | Southeastern Mixed Forest | 2 |
| 232 | Outer Coastal Plain Mixed Forest | 2 |
| 234 | Lower Mississippi Riverine Forest | 2 |
| 242 | Pacific Lowland Mixed Forest | NA |
| 251 | Prairie Parkland (Temperate) | 2 |
| 255 | Prairie Parkland (Subtropical) | NA |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | NA |
| 332 | Great Plains Steppe | 2 |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | NA |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 2 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 2 |
| M223 | Ozark Broadleaf Forest Meadow | 2 |
| M231 | Ouachita Mixed Forest | 2 |
| M242 | Cascade Mixed Forest | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 2 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M334 | Black Hills Coniferous Forest | 2 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | tau | tau.variance | tau.2.5 | tau.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | A | A.2.5 | A.97.5 | k | k.2.5 | k.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 4834 | 2417 | 0.1279608 | 0.0284711 | -0.2028349 | 0.4587565 | 0.6321701 | 0.0016604 | 0.5522851 | 0.7120551 | 3.590326 | 3.3408714 | 3.839781 | 7.388327 | 5.8507221 | 8.925931 |
| 212 | Laurentian Mixed Forest | east | 12976 | 6488 | 1.2642107 | 0.0454952 | 0.8461062 | 1.6823151 | 0.8107858 | 0.0008149 | 0.7548275 | 0.8667442 | 2.535356 | 2.3553622 | 2.715350 | 10.234816 | 9.0640147 | 11.405618 |
| 221 | Eastern Broadleaf Forest | east | 5462 | 2731 | -0.7424615 | 0.0187962 | -1.0112313 | -0.4736918 | 0.7142025 | 0.0021574 | 0.6231456 | 0.8052594 | 5.150452 | 4.7794010 | 5.521503 | 15.748319 | 11.8972981 | 19.599340 |
| 222 | Midwest Broadleaf Forest | east | 3554 | 1777 | 0.1376068 | 0.0738143 | -0.3951269 | 0.6703405 | 0.8472928 | 0.0029368 | 0.7410318 | 0.9535539 | 4.273414 | 3.7827200 | 4.764108 | 20.200006 | 16.1258614 | 24.274150 |
| 223 | Central Interior Broadleaf Forest | east | 6390 | 3195 | -1.0553007 | 0.0128260 | -1.2773216 | -0.8332798 | 0.6590475 | 0.0026282 | 0.5585451 | 0.7595500 | 5.756372 | 5.3059987 | 6.206746 | 35.129260 | 28.0590337 | 42.199487 |
| 231 | Southeastern Mixed Forest | east | 8200 | 4100 | 0.8497628 | 0.0309895 | 0.5046821 | 1.1948436 | 0.8717944 | 0.0008394 | 0.8150015 | 0.9285873 | 4.530354 | 4.2552477 | 4.805460 | 1.770008 | 1.2252304 | 2.314785 |
| 232 | Outer Coastal Plain Mixed Forest | east | 8194 | 4097 | 0.6928677 | 0.0353768 | 0.3241676 | 1.0615678 | 0.8713182 | 0.0008513 | 0.8141241 | 0.9285124 | 4.593184 | 4.2723916 | 4.913976 | 7.190935 | 5.9138993 | 8.467970 |
| 234 | Lower Mississippi Riverine Forest | east | 862 | 431 | 0.8586775 | 0.6736916 | -0.7523880 | 2.4697431 | 0.8321212 | 0.0110883 | 0.6254332 | 1.0388092 | 4.074795 | 2.8566903 | 5.292899 | 1.559619 | -0.7606964 | 3.879934 |
| 242 | Pacific Lowland Mixed Forest | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 251 | Prairie Parkland (Temperate) | east | 1394 | 697 | -0.0283305 | 0.2858016 | -1.0774338 | 1.0207728 | 0.4552015 | 0.0240589 | 0.1508159 | 0.7595870 | 3.432175 | 2.6128668 | 4.251482 | 12.153899 | 4.6990033 | 19.608794 |
| 255 | Prairie Parkland (Subtropical) | east | 446 | 223 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 118 | 59 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 332 | Great Plains Steppe | interior west | 154 | 77 | 1.5868390 | 6.3786373 | -3.4080136 | 6.5816916 | 0.5937701 | NA | -0.0069897 | 1.1945298 | 2.984765 | 0.3932120 | 5.576318 | 58.321034 | 10.3693915 | 106.272676 |
| 341 | Intermountain Semi-Desert and Desert | interior west | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 2 | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 411 | Everglades | east | 66 | 33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 5104 | 2552 | 0.8685334 | 0.0506691 | 0.4272444 | 1.3098225 | 0.6406419 | 0.0011902 | 0.5730094 | 0.7082744 | 2.935133 | 2.6981928 | 3.172073 | 2.856217 | 1.9139315 | 3.798502 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 5262 | 2631 | -0.0992304 | 0.0363308 | -0.4728986 | 0.2744378 | 0.8268867 | 0.0052096 | 0.6853889 | 0.9683845 | 4.382597 | 4.0080362 | 4.757157 | 9.421972 | 5.2945838 | 13.549361 |
| M223 | Ozark Broadleaf Forest Meadow | east | 604 | 302 | 3.0862540 | 2.8322583 | -0.2189318 | 6.3914398 | 0.9648155 | 0.0425404 | 0.5597454 | 1.3698855 | 1.896405 | 1.0433144 | 2.749496 | 9.275595 | -2.9165719 | 21.467762 |
| M231 | Ouachita Mixed Forest | east | 678 | 339 | 6.6083045 | 14.3861669 | -0.8391767 | 14.0557857 | 0.9277099 | 0.0235722 | 0.6262449 | 1.2291749 | 1.236440 | 0.4033305 | 2.069550 | 3.471953 | -0.5004547 | 7.444361 |
| M242 | Cascade Mixed Forest | pacific | 34 | 17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 340 | 170 | -1.4848044 | 6.6812476 | -6.5886030 | 3.6189941 | 0.6876548 | 0.1421385 | -0.0567697 | 1.4320792 | 14.505455 | -15.8203490 | 44.831260 | 350.454245 | -251.6671657 | 952.575656 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 8 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 20 | 10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 22 | 11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M334 | Black Hills Coniferous Forest | interior west | 310 | 155 | 0.2157499 | 2.5482666 | -2.9306267 | 3.3621265 | 0.8898888 | 0.0262753 | 0.5703953 | 1.2093823 | 2.553540 | 0.7680136 | 4.339067 | 39.577528 | 10.6673265 | 68.487729 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot tau
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning: package 'ggnewscale' was built under R version 4.2.1## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot alpha (biomass growth compensation effect)
plot A (asymptote of forest biomass growth in Mg/ha/yr)
## Warning: Removed 20 rows containing missing values (`geom_point()`).
plot k (stand biomass at half biomss G in Mg/ha)
## Warning: Removed 20 rows containing missing values (`geom_point()`).
Caclulations - weighted averages
tau (productivity trend (in %) 2000-2021)
## region weighted.tau weighted.tau.std_Error 95 % CI, upper
## 1 entire US 0.463104049 0.079886499 0.61968159
## 2 pacific -0.007761654 0.013511819 0.01872151
## 3 east 0.466080247 0.078138581 0.61923187
## 4 interior west 0.004785457 0.009677066 0.02375251
## 95 % CI, lower
## 1 0.30652651
## 2 -0.03424482
## 3 0.31292863
## 4 -0.01418159alpha (biomass growth compensation effect)
## region weighted.alpha weighted.alpha.std_Error 95 % CI, upper
## 1 entire US 0.765261331 0.0135256930 0.791771689
## 2 pacific 0.003594641 0.0019707926 0.007457394
## 3 east 0.756019476 0.0133590222 0.782203159
## 4 interior west 0.005647214 0.0007725766 0.007161465
## 95 % CI, lower
## 1 0.7387509726
## 2 -0.0002681127
## 3 0.7298357923
## 4 0.0041329641A (asymptote of forest biomass growth in Mg/ha/yr)
## region weighted.A
## 1 entire US 3.992012
## 2 pacific 13.047235
## 3 east 3.958788
## 4 interior west 1.961209K (stand biomass at half biomass G in Mg/ha)
## region weighted.k
## 1 entire US 13.14609
## 2 pacific 315.22340
## 3 east 11.16176
## 4 interior west 33.30795Analysis 2: Temporally-balanced, No-harvest
211 - Northeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3724 3417.2
## 2 3723 3391.4 1 25.757 28.276 1.114e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 14508.81
## 2 2 14482.61##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.03073 0.18885 0.163 0.871
## alpha 0.53669 0.09686 5.541 3.21e-08 ***
## A 3.59150 0.14566 24.657 < 2e-16 ***
## k 7.26908 0.86336 8.420 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9544 on 3723 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.194e-06
## (33 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_211, :
## object 'ge.fit' not found
## model AIC
## 1 2 14482.61
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.03073 0.18885 0.163 0.871
## alpha 0.53669 0.09686 5.541 3.21e-08 ***
## A 3.59150 0.14566 24.657 < 2e-16 ***
## k 7.26908 0.86336 8.420 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9544 on 3723 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.194e-06
## (33 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.91849, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -8.0619, p-value = 7.514e-16
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 15 rows containing missing values (`geom_point()`).## Warning: Removed 1050 rows containing missing values (`geom_line()`).
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7890 7621.3
## 2 7889 7527.4 1 93.969 98.484 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 28703.69
## 2 2 28607.77##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.95442 0.21791 4.38 1.2e-05 ***
## alpha 0.60286 0.05787 10.42 < 2e-16 ***
## A 2.66571 0.10561 25.24 < 2e-16 ***
## k 12.61290 0.77788 16.21 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9768 on 7889 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.864e-06
## (2589 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_212, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_212, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_212, :
## object 'ge.fit' not found
## model AIC
## 1 2 28607.77
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.95442 0.21791 4.38 1.2e-05 ***
## alpha 0.60286 0.05787 10.42 < 2e-16 ***
## A 2.66571 0.10561 25.24 < 2e-16 ***
## k 12.61290 0.77788 16.21 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9768 on 7889 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.864e-06
## (2589 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 1313 rows containing missing values (`geom_point()`).## Warning: Removed 1031 rows containing missing values (`geom_line()`).
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4441 6146.1
## 2 4440 6085.7 1 60.442 44.097 3.5e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 19769.06
## 2 2 19727.14##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.82538 0.14806 -5.575 2.63e-08 ***
## alpha 0.60176 0.08747 6.880 6.83e-12 ***
## A 5.25079 0.21634 24.271 < 2e-16 ***
## k 17.27045 2.28363 7.563 4.77e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.171 on 4440 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.773e-06
## (32 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_221, :
## object 'ge.fit' not found
## model AIC
## 1 2 19727.14
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.82538 0.14806 -5.575 2.63e-08 ***
## alpha 0.60176 0.08747 6.880 6.83e-12 ***
## A 5.25079 0.21634 24.271 < 2e-16 ***
## k 17.27045 2.28363 7.563 4.77e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.171 on 4440 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.773e-06
## (32 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.87959, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -11.423, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 14 rows containing missing values (`geom_point()`).## Warning: Removed 1036 rows containing missing values (`geom_line()`).
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2144 2308.5
## 2 2143 2245.5 1 63.03 60.154 1.346e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 8572.838
## 2 2 8515.402##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.07442 0.28535 -0.261 0.794
## alpha 0.76489 0.09167 8.344 <2e-16 ***
## A 4.37310 0.28154 15.533 <2e-16 ***
## k 21.10209 2.35205 8.972 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.024 on 2143 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.475e-06
## (651 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_222, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_222, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_222, :
## object 'ge.fit' not found
## model AIC
## 1 2 8515.402
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.07442 0.28535 -0.261 0.794
## alpha 0.76489 0.09167 8.344 <2e-16 ***
## A 4.37310 0.28154 15.533 <2e-16 ***
## k 21.10209 2.35205 8.972 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.024 on 2143 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.475e-06
## (651 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.91322, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.2187, p-value = 5.014e-10
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 340 rows containing missing values (`geom_point()`).## Warning: Removed 1108 rows containing missing values (`geom_line()`).
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4276 5695.6
## 2 4275 5657.3 1 38.24 28.896 8.043e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 18182.73
## 2 2 18155.91##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.14606 0.12313 -9.307 < 2e-16 ***
## alpha 0.54024 0.09692 5.574 2.64e-08 ***
## A 5.71873 0.25314 22.591 < 2e-16 ***
## k 33.51573 3.88016 8.638 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.15 on 4275 degrees of freedom
##
## Number of iterations to convergence: 13
## Achieved convergence tolerance: 8.25e-06
## (843 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_223, :
## object 'ge.fit' not found
## model AIC
## 1 2 18155.91
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.14606 0.12313 -9.307 < 2e-16 ***
## alpha 0.54024 0.09692 5.574 2.64e-08 ***
## A 5.71873 0.25314 22.591 < 2e-16 ***
## k 33.51573 3.88016 8.638 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.15 on 4275 degrees of freedom
##
## Number of iterations to convergence: 13
## Achieved convergence tolerance: 8.25e-06
## (843 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 408 rows containing missing values (`geom_point()`).## Warning: Removed 1002 rows containing missing values (`geom_line()`).
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6314 14258
## 2 6313 14075 1 182.61 81.904 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 32889.13
## 2 2 32809.70##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.75145 0.19412 3.871 0.000109 ***
## alpha 0.73004 0.07694 9.489 < 2e-16 ***
## A 4.53651 0.15788 28.733 < 2e-16 ***
## k 2.16383 0.35586 6.081 1.27e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.493 on 6313 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 9.246e-06
## (127 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_231, :
## object 'ge.fit' not found
## model AIC
## 1 2 32809.7
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.75145 0.19412 3.871 0.000109 ***
## alpha 0.73004 0.07694 9.489 < 2e-16 ***
## A 4.53651 0.15788 28.733 < 2e-16 ***
## k 2.16383 0.35586 6.081 1.27e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.493 on 6313 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 9.246e-06
## (127 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 60 rows containing missing values (`geom_point()`).## Warning: Removed 1017 rows containing missing values (`geom_line()`).
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6387 15897
## 2 6386 15679 1 217.93 88.761 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 33025.57
## 2 2 32939.37##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.54711 0.20463 2.674 0.00752 **
## alpha 0.67053 0.06749 9.935 < 2e-16 ***
## A 4.68597 0.18895 24.801 < 2e-16 ***
## k 8.91008 0.84283 10.572 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.567 on 6386 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.527e-06
## (150 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_232, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_232, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_232, :
## object 'ge.fit' not found
## model AIC
## 1 2 32939.37
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.54711 0.20463 2.674 0.00752 **
## alpha 0.67053 0.06749 9.935 < 2e-16 ***
## A 4.68597 0.18895 24.801 < 2e-16 ***
## k 8.91008 0.84283 10.572 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.567 on 6386 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.527e-06
## (150 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 75 rows containing missing values (`geom_point()`).## Warning: Removed 931 rows containing missing values (`geom_line()`).
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 684 1785.1
## 2 683 1734.9 1 50.129 19.734 1.038e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3613.269
## 2 2 3595.701##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.25438 0.71374 0.356 0.722
## alpha 0.81570 0.17001 4.798 1.97e-06 ***
## A 4.38706 0.64951 6.754 3.07e-11 ***
## k 0.08434 0.20431 0.413 0.680
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.594 on 683 degrees of freedom
##
## Number of iterations to convergence: 16
## Achieved convergence tolerance: 7.72e-06
## (27 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_234, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_234, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_234, :
## object 'ge.fit' not found
## model AIC
## 1 2 3595.701
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.25438 0.71374 0.356 0.722
## alpha 0.81570 0.17001 4.798 1.97e-06 ***
## A 4.38706 0.64951 6.754 3.07e-11 ***
## k 0.08434 0.20431 0.413 0.680
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.594 on 683 degrees of freedom
##
## Number of iterations to convergence: 16
## Achieved convergence tolerance: 7.72e-06
## (27 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93551, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.6254, p-value = 0.008654
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 14 rows containing missing values (`geom_point()`).## Warning: Removed 645 rows containing missing values (`geom_line()`).
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_242$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_242.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 868 1343.8
## 2 867 1338.7 1 5.0919 3.2977 0.06972 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3738.098
## 2 2 3736.791##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.08182 0.57699 -0.142 0.88727
## alpha 0.39484 0.20828 1.896 0.05832 .
## A 3.38977 0.44598 7.601 7.64e-14 ***
## k 11.46044 3.96871 2.888 0.00398 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.243 on 867 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 8.542e-06
## (349 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_251, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_251, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_251, :
## object 'ge.fit' not found
## model AIC
## 1 2 3736.791
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.08182 0.57699 -0.142 0.88727
## alpha 0.39484 0.20828 1.896 0.05832 .
## A 3.38977 0.44598 7.601 7.64e-14 ***
## k 11.46044 3.96871 2.888 0.00398 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.243 on 867 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 8.542e-06
## (349 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.71043, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.4474, p-value = 0.0005661
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 159 rows containing missing values (`geom_point()`).## Warning: Removed 1176 rows containing missing values (`geom_line()`).
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Error in nls(fg_1, data = G_255, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_255, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_255$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_255.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
add p model: does not fit
add s model: does not fit
add s+p model: does not fit
note: model fit, but fit was funky due to data being sparse
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_313.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
model selection 1
## Error in nls(fg_1, data = G_331, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_331, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_331.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
332 - Great Plains Steppe
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 128 101.432
## 2 127 98.965 1 2.4665 3.1652 0.07762 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 420.8587
## 2 2 419.6338##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.8695 1.9102 0.455 0.6497
## alpha 0.5777 0.2932 1.970 0.0510 .
## A 3.4946 1.3377 2.612 0.0101 *
## k 58.0814 22.5453 2.576 0.0111 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8828 on 127 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 7.549e-06
## (15 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_332, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_332, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_332, :
## object 'ge.fit' not found
## model AIC
## 1 2 419.6338
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.8695 1.9102 0.455 0.6497
## alpha 0.5777 0.2932 1.970 0.0510 .
## A 3.4946 1.3377 2.612 0.0101 *
## k 58.0814 22.5453 2.576 0.0111 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8828 on 127 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 7.549e-06
## (15 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89705, p-value = 4.933e-08
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.1251, p-value = 0.2605
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 7 rows containing missing values (`geom_point()`).## Warning: Removed 1140 rows containing missing values (`geom_line()`).
plotting 2
341 - Intermountain Semi-desert & Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
model selection 1
## Error in nls(fg_1, data = G_342, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_342, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_342$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_342.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
411 - Everglades
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3950 3001.9
## 2 3949 2983.3 1 18.581 24.596 7.366e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 14658.10
## 2 2 14635.55##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.94921 0.26165 3.628 0.00029 ***
## alpha 0.45788 0.08907 5.140 2.87e-07 ***
## A 2.81257 0.13373 21.032 < 2e-16 ***
## k 2.40480 0.47337 5.080 3.95e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8692 on 3949 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 2.432e-06
## (13 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## model AIC
## 1 2 14635.55
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.94921 0.26165 3.628 0.00029 ***
## alpha 0.45788 0.08907 5.140 2.87e-07 ***
## A 2.81257 0.13373 21.032 < 2e-16 ***
## k 2.40480 0.47337 5.080 3.95e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8692 on 3949 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 2.432e-06
## (13 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.9814, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -7.6036, p-value = 2.879e-14
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 6 rows containing missing values (`geom_point()`).## Warning: Removed 1108 rows containing missing values (`geom_line()`).
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4659 10327
## 2 4658 10238 1 89.796 40.857 1.798e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 23096.71
## 2 2 23057.99##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.1667 0.1983 -0.841 0.401
## alpha 0.8093 0.1215 6.663 2.99e-11 ***
## A 4.4088 0.2043 21.579 < 2e-16 ***
## k 9.8033 2.2913 4.279 1.92e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.483 on 4658 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 5.654e-06
## (26 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## model AIC
## 1 2 23057.99
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.1667 0.1983 -0.841 0.401
## alpha 0.8093 0.1215 6.663 2.99e-11 ***
## A 4.4088 0.2043 21.579 < 2e-16 ***
## k 9.8033 2.2913 4.279 1.92e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.483 on 4658 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 5.654e-06
## (26 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.86979, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.5674, p-value = 5.122e-11
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 15 rows containing missing values (`geom_point()`).## Warning: Removed 982 rows containing missing values (`geom_line()`).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 524 819.79
## 2 523 814.78 1 5.0148 3.219 0.07337 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2249.276
## 2 2 2248.042##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.6120 1.5870 1.646 0.1004
## alpha 0.7193 0.3836 1.875 0.0613 .
## A 2.1379 0.5145 4.156 3.79e-05 ***
## k 18.4775 10.2802 1.797 0.0729 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.248 on 523 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 5.752e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## model AIC
## 1 2 2248.042
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.6120 1.5870 1.646 0.1004
## alpha 0.7193 0.3836 1.875 0.0613 .
## A 2.1379 0.5145 4.156 3.79e-05 ***
## k 18.4775 10.2802 1.797 0.0729 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.248 on 523 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 5.752e-06
## (3 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.9264, p-value = 2.132e-15
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.3291, p-value = 0.1838
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 3 rows containing missing values (`geom_point()`).## Warning: Removed 1175 rows containing missing values (`geom_line()`).
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 581 732.19
## 2 580 722.39 1 9.7989 7.8674 0.005202 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2364.027
## 2 2 2358.158##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 5.5161 3.1963 1.726 0.08492 .
## alpha 1.0485 0.3536 2.965 0.00315 **
## A 1.6402 0.5390 3.043 0.00245 **
## k 21.4176 7.0809 3.025 0.00260 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 580 degrees of freedom
##
## Number of iterations to convergence: 25
## Achieved convergence tolerance: 7.903e-06
## (4 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## model AIC
## 1 2 2358.158
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 5.5161 3.1963 1.726 0.08492 .
## alpha 1.0485 0.3536 2.965 0.00315 **
## A 1.6402 0.5390 3.043 0.00245 **
## k 21.4176 7.0809 3.025 0.00260 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 580 degrees of freedom
##
## Number of iterations to convergence: 25
## Achieved convergence tolerance: 7.903e-06
## (4 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.97305, p-value = 6.907e-09
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.8918, p-value = 0.00383
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 2 rows containing missing values (`geom_point()`).## Warning: Removed 1218 rows containing missing values (`geom_line()`).
plotting 2
M242 - Cascade Mixed Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 156 186.49
## 2 155 182.34 1 4.1465 3.5247 0.06234 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 555.0482
## 2 2 553.4730##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.7886 2.1337 -0.838 0.4032
## alpha 0.7789 0.3841 2.028 0.0443 *
## A 13.3571 12.6238 1.058 0.2917
## k 270.2899 210.0776 1.287 0.2001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.085 on 155 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 6.586e-06
## (163 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## model AIC
## 1 2 553.473
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.7886 2.1337 -0.838 0.4032
## alpha 0.7789 0.3841 2.028 0.0443 *
## A 13.3571 12.6238 1.058 0.2917
## k 270.2899 210.0776 1.287 0.2001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.085 on 155 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 6.586e-06
## (163 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96531, p-value = 0.0005074
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 1.5145, p-value = 0.1299
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 83 rows containing missing values (`geom_point()`).## Warning: Removed 1274 rows containing missing values (`geom_line()`).
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_M313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M313.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_M331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M331.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 180 187.48
## 2 179 177.39 1 10.092 10.184 0.001673 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 561.9578
## 2 2 553.8317##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.8928 2.3638 0.378 0.70609
## alpha 0.8966 0.2556 3.508 0.00057 ***
## A 2.2580 1.0249 2.203 0.02885 *
## k 41.5917 17.1706 2.422 0.01642 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9955 on 179 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 5.03e-06
## (75 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## model AIC
## 1 2 553.8317
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.8928 2.3638 0.378 0.70609
## alpha 0.8966 0.2556 3.508 0.00057 ***
## A 2.2580 1.0249 2.203 0.02885 *
## k 41.5917 17.1706 2.422 0.01642 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9955 on 179 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 5.03e-06
## (75 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.72277, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.48726, p-value = 0.6261
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 35 rows containing missing values (`geom_point()`).## Warning: Removed 1 rows containing missing values (`geom_segment()`).## Warning: Removed 1264 rows containing missing values (`geom_line()`).
plotting 2
## Warning: Removed 1 rows containing missing values (`geom_segment()`).
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 2 |
| 212 | Laurentian Mixed Forest | 2 |
| 221 | Eastern Broadleaf Forest | 2 |
| 222 | Midwest Broadleaf Forest | 2 |
| 223 | Central Interior Broadleaf Forest | 2 |
| 231 | Southeastern Mixed Forest | 2 |
| 232 | Outer Coastal Plain Mixed Forest | 2 |
| 234 | Lower Mississippi Riverine Forest | 2 |
| 242 | Pacific Lowland Mixed Forest | NA |
| 251 | Prairie Parkland (Temperate) | 2 |
| 255 | Prairie Parkland (Subtropical) | NA |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | NA |
| 332 | Great Plains Steppe | 2 |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | NA |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 2 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 2 |
| M223 | Ozark Broadleaf Forest Meadow | 2 |
| M231 | Ouachita Mixed Forest | 2 |
| M242 | Cascade Mixed Forest | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 2 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M334 | Black Hills Coniferous Forest | 2 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | tau | tau.variance | tau.2.5 | tau.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | A | A.2.5 | A.97.5 | k | k.2.5 | k.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 4834 | 2417 | 0.0307348 | 0.0356626 | -0.3395158 | 0.4009854 | 0.5366872 | 0.0093811 | 0.3467912 | 0.7265832 | 3.591496 | 3.3059187 | 3.877073 | 7.2690758 | 5.5763777 | 8.9617738 |
| 212 | Laurentian Mixed Forest | east | 12976 | 6488 | 0.9544216 | 0.0474836 | 0.5272655 | 1.3815778 | 0.6028578 | 0.0033489 | 0.4894187 | 0.7162968 | 2.665713 | 2.4586869 | 2.872739 | 12.6128998 | 11.0880403 | 14.1377594 |
| 221 | Eastern Broadleaf Forest | east | 5462 | 2731 | -0.8253826 | 0.0219215 | -1.1156522 | -0.5351130 | 0.6017614 | 0.0076507 | 0.4302801 | 0.7732428 | 5.250788 | 4.8266466 | 5.674929 | 17.2704506 | 12.7934007 | 21.7475005 |
| 222 | Midwest Broadleaf Forest | east | 3554 | 1777 | -0.0744190 | 0.0814267 | -0.6340178 | 0.4851798 | 0.7648889 | 0.0084042 | 0.5851089 | 0.9446689 | 4.373100 | 3.8209878 | 4.925213 | 21.1020876 | 16.4895458 | 25.7146294 |
| 223 | Central Interior Broadleaf Forest | east | 6390 | 3195 | -1.1460557 | 0.0151617 | -1.3874596 | -0.9046517 | 0.5402401 | 0.0093929 | 0.3502321 | 0.7302480 | 5.718731 | 5.2224514 | 6.215011 | 33.5157251 | 25.9086070 | 41.1228432 |
| 231 | Southeastern Mixed Forest | east | 8200 | 4100 | 0.7514525 | 0.0376820 | 0.3709141 | 1.1319910 | 0.7300422 | 0.0059191 | 0.5792227 | 0.8808616 | 4.536512 | 4.2270053 | 4.846018 | 2.1638330 | 1.4662259 | 2.8614400 |
| 232 | Outer Coastal Plain Mixed Forest | east | 8194 | 4097 | 0.5471111 | 0.0418722 | 0.1459734 | 0.9482488 | 0.6705347 | 0.0045549 | 0.5382322 | 0.8028372 | 4.685965 | 4.3155689 | 5.056362 | 8.9100761 | 7.2578408 | 10.5623113 |
| 234 | Lower Mississippi Riverine Forest | east | 862 | 431 | 0.2543828 | 0.5094240 | -1.1470042 | 1.6557698 | 0.8156986 | 0.0289048 | 0.4818855 | 1.1495117 | 4.387056 | 3.1117811 | 5.662330 | 0.0843379 | -0.3168208 | 0.4854966 |
| 242 | Pacific Lowland Mixed Forest | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 251 | Prairie Parkland (Temperate) | east | 1394 | 697 | -0.0818190 | 0.3329193 | -1.2142826 | 1.0506445 | 0.3948426 | NA | -0.0139415 | 0.8036267 | 3.389765 | 2.5144444 | 4.265086 | 11.4604392 | 3.6710413 | 19.2498372 |
| 255 | Prairie Parkland (Subtropical) | east | 446 | 223 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 118 | 59 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 332 | Great Plains Steppe | interior west | 154 | 77 | 0.8695397 | 3.6488425 | -2.9103902 | 4.6494696 | 0.5777227 | NA | -0.0024404 | 1.1578859 | 3.494613 | 0.8476030 | 6.141623 | 58.0814043 | 13.4683245 | 102.6944841 |
| 341 | Intermountain Semi-Desert and Desert | interior west | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 2 | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 411 | Everglades | east | 66 | 33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 5104 | 2552 | 0.9492095 | 0.0684631 | 0.4362188 | 1.4622003 | 0.4578758 | 0.0079339 | 0.2832433 | 0.6325083 | 2.812567 | 2.5503888 | 3.074746 | 2.4047995 | 1.4767252 | 3.3328738 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 5262 | 2631 | -0.1667323 | 0.0393256 | -0.5555075 | 0.2220430 | 0.8093399 | 0.0147544 | 0.5712057 | 1.0474742 | 4.408786 | 4.0082448 | 4.809327 | 9.8032695 | 5.3112649 | 14.2952740 |
| M223 | Ozark Broadleaf Forest Meadow | east | 604 | 302 | 2.6120500 | 2.5186583 | -0.5056830 | 5.7297829 | 0.7193302 | NA | -0.0342572 | 1.4729176 | 2.137883 | 1.1272249 | 3.148541 | 18.4775170 | -1.7180528 | 38.6730867 |
| M231 | Ouachita Mixed Forest | east | 678 | 339 | 5.5161264 | 10.2166025 | -0.7616891 | 11.7939420 | 1.0484776 | 0.1250285 | 0.3539974 | 1.7429579 | 1.640184 | 0.5814777 | 2.698890 | 21.4175745 | 7.5103402 | 35.3248087 |
| M242 | Cascade Mixed Forest | pacific | 34 | 17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 340 | 170 | -1.7886265 | 4.5525652 | -6.0034589 | 2.4262058 | 0.7788800 | 0.1475292 | 0.0201432 | 1.5376168 | 13.357096 | -11.5797974 | 38.293990 | 270.2898590 | -144.6946651 | 685.2743832 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 8 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 20 | 10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 22 | 11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M334 | Black Hills Coniferous Forest | interior west | 310 | 155 | 0.8928341 | 5.5874617 | -3.7716283 | 5.5572964 | 0.8966265 | 0.0653123 | 0.3923231 | 1.4009300 | 2.258047 | 0.2356876 | 4.280407 | 41.5916937 | 7.7088672 | 75.4745202 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot tau
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot alpha (biomass growth compensation effect)
plot A (asymptote of forest biomass growth in Mg/ha/yr)
## Warning: Removed 20 rows containing missing values (`geom_point()`).
plot k (stand biomass at half biomss G in Mg/ha)
## Warning: Removed 20 rows containing missing values (`geom_point()`).
Caclulations - weighted averages
tau (stand biomass growth enhancement factor in % 2000-2021)
## region weighted.tau weighted.tau.std_Error 95 % CI, upper
## 1 entire US 0.311719729 0.08009314 0.46870229
## 2 pacific -0.009349851 0.01115355 0.01251111
## 3 east 0.314755388 0.07837811 0.46837649
## 4 interior west 0.006314192 0.01214007 0.03010873
## 95 % CI, lower
## 1 0.15473717
## 2 -0.03121081
## 3 0.16113429
## 4 -0.01748035alpha (biomass growth compensation effect)
## region weighted.alpha weighted.alpha.std_Error 95 % CI, upper
## 1 entire US 0.630077177 0.026282736 0.681591340
## 2 pacific 0.004071511 0.002007816 0.008006831
## 3 east 0.620364334 0.026177609 0.671672449
## 4 interior west 0.005641332 0.001218051 0.008028712
## 95 % CI, lower
## 1 0.578563015
## 2 0.000136191
## 3 0.569056220
## 4 0.003253953A (asymptote of forest biomass growth in Mg/ha/yr)
## region weighted.A
## 1 entire US 4.036723
## 2 pacific 12.014319
## 3 east 4.010511
## 4 interior west 1.940697K (stand biomass at half biomass G in Mg/ha)
## region weighted.k
## 1 entire US 13.71999
## 2 pacific 243.11786
## 3 east 12.16129
## 4 interior west 34.22878